Injection molding machines allow resin products to be manufactured while molding conditions are adjusted. When unsatisfactory products have been produced, the molding conditions are corrected to ensure that satisfactory products are obtained. The molding control method that uses a neural network and is disclosed, for example, in Japanese Patent Laid-Open Publication No. HEI-5-309711 is proposed as a technique for correcting molding conditions. The use of a neural network makes it possible to efficiently deal with nonuniform monitor values and to reduce the time and cost of test runs.
The principle of a neural network is described below with reference to FIG. 6 hereof.
FIG. 6 shows an example of a layered neural network configured from an input layer 100 composed of four input units, an intermediate layer 110 composed of one layer having five units, and an output layer 120 composed of one output unit.
In the input layer 100, for example, a monitor value S1 related to the farthest forward location of injection as determined by a sensor provided to the injection molding machine, a monitor value S2 related to the location at the start of weighing, a monitor value S3 related to the temperature of the opening through which material falls, and a monitor value S4 related to peak loading pressure are inputted, respectively, to first, second, third, and fourth input units 101, 102, 103, and 104.
The value of the first unit 111 of the intermediate layer 110 is determined by processing the monitor values S1, S2, S3, and S4 with the aid of a threshold value and the weighting factors determined for each input. The value of the second unit 112 of the intermediate layer 110 is determined by processing the monitor values S1, S2, S3, and S4 with the aid of another threshold value and the weighting factors determined for each input. The values of the third through fifth units 113 through 115 are determined in the same manner.
The output unit 121 of the output layer 120 is determined by processing the values of the first through fifth units 111 through 115 of the intermediate layer 110 with the aid of yet another threshold and the weighting factors determined for each of the first through fifth units 111 through 115. This output unit 121 has a predicted weight obtained from the predicted quality value of the molded articles in this example.
Since the neural network is a function, the monitor values S1 through S4 inputted to the input layer 100 and the output layer 120 can be assumed to be known quantities, and the weighting factors and thresholds in the function can be assumed to be unknown quantities. Specifically, the monitor values S1 through S4 are provided to the input layer 100, and the measured weight of the molded articles is provided to the output layer 120. The weight predicted by a computer is repeatedly calculated while revising the weighting factors and the thresholds until the predicted weight matches the measured weight. When the predicted weight satisfactorily matches the measured weight, the weighting factors and the thresholds are determined. When the weighting factors and the thresholds are determined, the function, or, specifically, the quality prediction function, is established.
Thus, if a neural network is used, a quality prediction function can be established by estimating the weighting factors and the thresholds in addition to calculating the predicted weights.
The inventors conducted confirmation experiments using an injection molding machine having a neural network with the object of confirming the precision of the neural network. Summaries of the experiments are as follows.
(1) Molding conditions are set with a high probability that satisfactory products will be obtained.
(2) 20 shots are conducted. At this time, the monitor values are obtained by a sensor in the injection molding machine.
(3) The weight of the molded article is measured for each shot.
(4) The weighting factors and the thresholds in the neural network are determined using the monitor values as the input of the neural network, and the weights of the molded articles as the output of the neural network. The weighting factor and threshold used in one shot are corrected for the next text molding. Repeating such corrections is referred to as “learning.”
(5) The neural network (quality prediction function) is established by means of the learning in these 20 shots. This neural network (quality prediction function) can also be said to be a weight prediction function in which the weighting factors and the thresholds are determined. Therefore, predicted weights can be outputted when the monitor values are inputted to the weight prediction function.
(6) Mass-production molding begins with the 21st shot. The weight prediction function is not revised with mass-production molding. The monitor values continue to be obtained by the sensor in the injection molding machine in mass-production molding.
(7) The weights of the molded articles are then measured. The weights as measured are referred to as the measured weights.
(8) The predicted weights are calculated by inputting the monitor values obtained in (6) to the neural network (weight prediction function) in which the learning process has been completed.
(9) The measured weights and the predicted weights Ws are compared to determine the “probability” of the predicted weights Ws.
The results of the confirmation test described above are shown in FIG. 7. The horizontal axis indicates the number of shots, the vertical axis indicates the weight of the molded articles, the bold-line graph represents the measured weights Wact, and the thin-line graph represents the predicted weights Ws.
The numbers 0 to 20 along the horizontal axis indicate the range of test moldings, and the numbers 21 and up indicate the range of mass-production moldings.
The weight prediction function is determined in the shots 1 through 20.
In the shots 21 through 50, the monitor values are inputted to the determined weight prediction function, and the predicted weights Ws are outputted, whereupon the predicted weights Ws are found to be very close to the measured weights Wact.
In the shots 51 through 110, the monitor values are inputted to the weight prediction function, and the predicted weights Ws are outputted, whereupon the predicted weights Ws are found deviate considerably from the measured weights Wact.
The shots 51 through 110 are believed to be affected by changes over time and by slight revisions in the molding conditions during mass-production molding.
It is necessary to avoid any loss in the predicted precision starting at a certain point in time in order to be able to continuously conduct multiple shots with an injection molding machine, and improved techniques are needed.